(2024) 22:287
Melnick et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03498-6

BMC Medicine

RESEARCH ARTICLE

Open Access

The relationship between community foodenvironment around schools and student
meal participation: the role of school CEP
participation status
Emily M. Melnick1* , Francesco Acciai1, Michael J. Yedidia2 and Punam Ohri‑Vachaspati1

Abstract
Background Despite the many benefits of school meals, not all students participate. One reason students may
not participate in school meals is because they instead purchase breakfast or lunch from food outlets locatedaround schools that mostly carry unhealthy items. This study examined whether school participation in the Commu‑
nity Eligibility Provision (CEP), which allows qualifying schools to serve free meals to all students, moderated the asso‑
ciation between the community food environment around schools and student meal participation.
Methods This study employed a longitudinal repeated-measures design using school-level data collected
between 2014 and 2020 within four low-income school districts (n = 126 schools) in the US. We obtained meal partici‑
pation data from state records and created a measure characterizing the community food environment within 0.25
miles of schools (characterized as low-density of unhealthy food outlets vs. high-density of unhealthy food outlets)
through a latent class analysis. Regression analysis estimated associations between community food environments,
CEP participation, and participation rates in school breakfast and school lunch, assessed in separate models.
Results While no moderating effect of school CEP status was observed for breakfast or lunch participation, school
breakfast participation was predicted to be 4% lower in high-density food environments than in low-density environ‑ments (P-value = .049) among non-CEP schools, and there was no difference in participation by the community foodenvironment among CEP-participating schools. Differences in breakfast participation by the community food environ‑ment among non-CEP schools were mostly attributable to middle/high schools, with participation predicted to be
10% lower in high-density environments than in low-density environments among non-CEP middle/high schools(P-value < .001), whereas such a difference in participation was not observed among non-CEP elementary schools.
Conclusions Negative associations between food environment around schools and school breakfast participa‑tion were observed only among middle and high schools not participating in CEP, suggesting that policy actions
to increase access to free school meals may benefit students, particularly older children and adolescents.
Keywords School meal participation, Food environment, Community eligibility provision*Correspondence:
Emily M. Melnick
emily.melnick@asu.edu
1
College of Health Solutions, Arizona State University, Phoenix, AZ, USA
2
Rutgers Center for State Health Policy, Rutgers University, New
Brunswick, NJ, USA

Background
School meals provide important nutritional support forschool-aged children and adolescents in the US, particularly for students living in families at increased riskof experiencing food and nutrition insecurity [1, 2].

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Melnick et al. BMC Medicine(2024) 22:287

Demonstrated benefits of school meals include improved
nutrition, academic, and behavioral outcomes [3, 4].
These benefits have been particularly pronounced since
the passage of the Healthy Hunger-Free Kids Act of 2010,
which requires that school breakfasts and lunches meethealthier nutrition standards [5]. Studies conducted following the passage of this legislation have shown thatboth lunches and breakfasts consumed at school are more
nutritious than meals brought from home or purchasedin other places [6–9]. However, despite the many benefits of school meals, not all students participate. A few
possible reasons for non-participation include perceived
stigma related to participating in school meals, bringing
food from home instead, and lack of availability of preferred food items [10]. Another reason students may notparticipate in school meals is because they instead purchase breakfast or lunch from food outlets, such as fastfood restaurants and convenience stores, which are often
located around schools [11–13]. Research conducted in
the US and other countries indicates that a higher density of fast-food outlets surrounding schools is associatedwith lower participation in school meal programs [14, 15]
and the foods that students tend to purchase from foodoutlets surrounding schools (including fast-food restaurants and convenience stores) are often of poorer nutritional quality compared to school meals [16, 17]. Given
the benefits of school meals and the differences in nutritional quality between school meals and food purchased
in retail outlets in the surrounding community, it isimportant to consider how school policies and programs
may boost school meal participation rates.
The Community Eligibility Provision (CEP) authorized
by the Healthy Hunger-Free Kids Act of 2010 allows US
schools and districts serving children from low-income
areas to offer meals at no cost to all enrolled students[18]. The aim of CEP is to improve access to nutritious
school meals among children living in low-income areas.
Schools are eligible to participate in CEP based on the
proportion of students whose families participate inother means-tested federal assistance programs; this proportion is known as the identified student percentage.
Schools were eligible to participate in CEP if they had
an identified student percentage of 40% or higher until
school year (SY) 2022–2023 [18]. Beginning October
26, 2023, the US Department of Agriculture expanded
access to CEP by lowering the identified student percentage requirement to 25% or greater [19]. Notably, not all
schools or districts that are eligible to participate in CEP
choose to do so. For some schools, participating is notfinancially viable due to the reimbursement structure,
which is dependent on the school’s identified student
percentage [20]. Additional concerns expressed by eligible schools opting not to participate in CEP include a lack

Page 2 of 13

of clarity about the implementation of CEP, anticipatedoperational challenges associated with implementing the
program, and uncertainty regarding the impacts of CEP
on school meal participation [21]. In SY 2019–2020, only
69% of eligible schools in the US opted to participate in
CEP [22].
Schools participating in CEP tend to have higher rates
of school meal participation, compared to similar, eligible schools that do not participate in CEP, in part due
to a normalization of eating school meals and reduction of associated stigma [3, 20, 23]. Consequently, one
may hypothesize that school participation in CEP may
lessen the attraction of unhealthy food outlets surrounding schools for students, thereby providing a “buffering
effect” against obesogenic community food environmentsthat are often associated with reduced student participation in school meals. It is also possible that older students
are more susceptible to influences of community foodenvironments surrounding schools, since they may havemore purchasing power to pick up foods from nearby
outlets [13] and are more likely to travel to school unsupervised by a parent [24]. To test these hypotheses, weinvestigated whether the association between the community food environment (specifically, the density of
unhealthy retail food outlets) around schools and school
meal participation rates differed based on schools’ CEP
participation status, using longitudinal data collected
over a 6-year period within four urban public school
districts in the US. Anticipating age-related differencesin consumption behaviors, we also investigated whether
this association was different for elementary vs. middle
and high schools.

Methods
This study employed a longitudinal repeated-measuresdesign to evaluate associations of interest using data collected from public schools between SY 2014–2015, when
CEP became available to eligible schools nationwide in
the US, and SY 2020–2021. Data were collected from all
public schools within four urban school districts in New
Jersey (Camden, New Brunswick, Newark, and Trenton)with predominantly low-income, high-minority populations. There were 126 public schools with complete dataon all collected variables across the four school districts
over the study period. There were 98 schools with complete data in SY 2014–2015, 98 in SY 2015–2016, 99 in
SY 2016–2017, 101 in SY 2017–2018, 84 in SY 2018–2019, and 91 in SY 2019–2020 (total n = 571 observations). In addition to non-response, which was limited,
the number of schools for each SY differed slightly fromyear to year due to school openings and closings. The
majority of schools in the sample (69.4%) were observedin all six study years. All schools within the study sample

Melnick et al. BMC Medicine(2024) 22:287

were low-income; 82.6% of students attending schools
not participating in CEP and 67.3% of students attending CEP-participating schools were eligible for free andreduced-price meals over the study period. Additionally,in 2019–2020, all schools within the study sample were
eligible to participate in CEP based on identified student
percentage.
Datasets

Longitudinal data were collected between SY 2014–15
and SY 2019–20 as part of the New Jersey Child HealthStudy and were used to characterize the community
food environment around schools and the food environment within schools [25]. Measures of the community
food environment around schools were based on lists offood outlets obtained from two commercial data companies, InfoUSA [26] and Trade Dimensions/Nielsen[27], for each study year within the cities where the four
school districts were located. Comprehensive school
data were collected through a 96-item survey, including
questions based on previous research [28, 29], that wascompleted by school nurses, in consultation with school
food staff. The survey could be completed through an
online survey platform (Qualtrics – Provo, UT, USA) orwith pen and paper. To determine school breakfast andlunch participation rates for each school for each of the
SYs, we obtained school-level average daily participation numbers in school breakfast and lunch and school
enrollment data from the New Jersey (NJ) Department ofAgriculture through Open Public Records Act requests.
For SY 2019–2020, average daily participation numbers
in school lunch and school breakfast programs were calculated based on participation between the start of theschool year and March 2020, when schools were closed
due to the COVID-19 pandemic. The NJ Department of
Education and Food Research & Action Center websites
provided data on school participation in CEP for each of
the SYs [30, 31]. Finally, data from the National Center for
Education Statistics common core data repository provided school-level characteristics, including the proportion of students eligible for free or reduced-price meals(FRPM) and the racial/ethnic composition of enrolled
students, for each of the SYs over the study period [32].
Outcome variables

The outcome variables for all analyses were the (1) school
breakfast participation rate and (2) school lunch participation rate. Participation rates were calculated for each of
the SYs under investigation by dividing the average daily
participation numbers in school breakfast and school
lunch by the number of students enrolled.

Page 3 of 13

Predictor variables

To characterize the community food environment
around schools, we created a measure of unhealthy foodoutlet density. For each study year, lists of food outlets
obtained from commercial databases were de-duplicated,
geocoded, and categorized into outlet types based on
the types of foods served and sales volume using a systematic classification protocol [33]. This study included
two types of food outlets that are both likely to be frequented by students and carry a majority of unhealthy
items: convenience stores and limited-service (i.e., fastfood) restaurants. All stores with a sales volume less than
$1 million and stores from larger chains recognized asconvenience stores (e.g., Wawa or 7-Eleven) were classified as convenience stores. Additionally, stores withsales volume between $1 and $2 million were classified
as convenience stores if they did not carry 3 or more of
the following healthy options: 5 different types of fruits,
5 different types of vegetables, low-fat or skim milk, andfresh or frozen meat. Restaurants in which patrons paid
before, rather than after, eating were classified as limitedservice restaurants.
To determine the prevalence of these two types of foodoutlets around schools, we calculated the number of outlets within a 0.25-mile roadway network—a walking distance consistent with previous literature [34, 35]—fromeach school. Distances were calculated using the mostdirect route by means of roadway networks obtainedusing NJ Road Network data from the NJ Department ofTransportation and NJ Office of GIS [36], excluding highways and toll roads as walking routes, using Geographical Information System ArcGIS software (Esri, Redlands,
CA).
Latent class analysis models (via generalized structural
equation modeling) were used to create the unhealthy
food outlet density measure across the assessed 6-year
period. Variables used to create this measure included(1) the number of convenience stores and (2) the numberof limited-service restaurants. These two variables were
upcoded (i.e., values were capped at the 97th percentileof the distribution if they were above this threshold) to
reduce the influence of extreme values on class assignments. Likelihood-ratio tests, the Akaike information criterion, and the Bayesian Information Criterion assessed
the latent class analysis model specification for 2-, 3-, and4-class models. A 2-class model (low-density environment vs. high-density environment) was retained basedon indicators of goodness of fit and because both the3- and 4-class models produced some classes that weredeemed too small to support regression models withinteraction terms. For example, one of the three classes
in the 3-class solution only had four CEP-participating
schools.

Melnick et al. BMC Medicine(2024) 22:287

School participation in CEP (yes vs. no) was treated asa dichotomous variable for each of the study years.
Control variables

A number of school-level variables were included ascontrols in the analyses. School food environment variables included the healthfulness of school foods served
at lunchtime, healthfulness of available competitive
foods, and breakfast delivery model (breakfast served in
the classroom vs. not) based on school staff responses to
New Jersey Child Health Study survey items. Consistent
with previous literature [34], we created two summary
measures (i.e., scales ranging from 0 to 1) to measureschool foods healthfulness based on responses to foodavailability questions, with higher scores corresponding
to healthier environments. The first scale, capturing thehealthfulness of school foods served at lunchtime (i.e.,
the NSLP healthy scale), was calculated as the ratio of
healthy NSLP items over the total (healthy + unhealthy)
number of NSLP items. The second scale, capturing thehealthfulness of a la carte foods and foods sold in vending
machines (i.e., the Competitive food healthy scale), wasalso calculated as the ratio of healthy items divided by the
total number of items (healthy + unhealthy). Additional
information on the within-school food environment data
and the full list of items captured within the scale measures is available elsewhere [37].
In addition, we included as control variables school
majority race/ethnicity grouped into three categories: (1)majority (> 50%) black, (2) majority (> 50%) Hispanic, and(3) majority (> 50%) non-Hispanic White/no majority
race/ethnicity; school level grouped into two categories:
elementary school and middle/high school; school enrollment operationalized continuously; the proportion of
students eligible for FRPM (ranging from 0 to 100); andschool year operationalized continuously.

Page 4 of 13

community food environment category (high-density
environment/low-density environment) and CEP participation (yes/no) were added in subsequent models to
examine whether the association between the community food environment around schools and (1) school
breakfast and (2) school lunch participation rates, weremoderated by CEP participation. To assess whether theseinteraction effects differed according to school level, wethen used a 3-way interaction term that included (i) thecommunity food environment category, (ii) CEP participation, and (iii) school level (elementary school/middle or high school). We used the margins command in
Stata to determine predicted participation rates in school
breakfast and school lunch; these rates were based on thecoefficients estimated from the models described above
and were used to construct the presented figures.
We conducted sensitivity analyses to ensure the robustness of findings. First, because Stata does not allow fora 3-level model or a second clustering variable using thextgee command, we ran three-level mixed-effects linear
regression models, which treated schools as repeatedobservations (as our main model did) that are also
nested within school districts. This was done to addressconcerns about limited variability in CEP participation
within districts. Other models treated the time variable as a series of six dummy variables, one for each SY,
instead of a continuous variable. Findings at P < 0.05 wereconsidered statistically significant.

Results
Latent class analysis

Table 1 depicts the two identified classes and theirrespective predicted mean numbers of convenience
stores and limited-service restaurants. The first identifiedclass (n = 487, 85% of the sample), labeled “low-density
environment,” was characterized by a relatively lower
mean number of convenience stores (1.6 stores) andStatistical analysis

Regression models for panel data assessed associations
between all predictor variables of interest and participation rates in school breakfast and lunch, analyzed inseparate models. Because school meal participation rates
are proportions, thus bounded between zero and one,standard linear models, which assume an unbounded
distribution of the outcome variable, may not provide
an accurate representation of the associations of interest. Therefore, using regression models for fractional
outcome variables is more appropriate for the present
study [38]. Specifically, we used the xtgee command in
Stata version 16 (StataCorp LLC, College Station, TX) to
run a generalized linear regression model for panel datawith a binomial distribution, probit link function, androbust standard errors. Interaction terms between the

Table 1 Composition of the analytic sample by community foodenvironment within a 0.25-mile roadway network surrounding
schools
Community food environment around school
Low-density
environment (n = 487
school observations)

High-density environment(n = 84 school
observations)

Conveni‑ence stores,
mean (SD)

1.6 (1.3)

3.7 (1.3)

Limitedservice
restaurants,
mean (SD)

1.4 (1.3)

7.3 (1.9)

Melnick et al. BMC Medicine(2024) 22:287

limited-service restaurants (1.4 restaurants). The second
identified class (n = 84, 15% of the sample), labeled “highdensity environment,” had comparatively more convenience stores and limited-service restaurants.
Descriptive results

Table 2 shows school-level characteristics of the sample
for each of the 6 years of the study period, stratified byschool CEP participation status. The majority of schools(ranging from 69 to 73% for each study year) were elementary schools. Most of the study schools served amajority of Black or Hispanic students. Over all six SYs
observed, 83% of students attending non-CEP schools
and 67% of students attending CEP schools qualified forfree or reduced-price meals. Over all six SYs observed,
over three-quarters of students in CEP schools participated in school lunch (78%) and 59% participated inschool breakfast. Of students attending non-CEP schools,
69% participated in school lunch and 57% participated inschool breakfast.
Model results

Results of initial multivariable regression models forpanel data assessing predictors of school breakfast andschool lunch participation rates without interaction
terms are presented in Table 3. In these models, the density of unhealthy food outlets around schools was not
associated with school breakfast participation rates orwith school lunch participation rates. School CEP participation was associated with both higher rates of school
breakfast participation (B = 0.12, P = 0.02) and school
lunch participation (B = 0.13, P < 0.01).
Subsequent models included an interaction term
between the community food environment aroundschools and school CEP participation to assess whether
the relationship between the food environment andschool meal participation varies by school CEP participation. Results of regression models including twoway interaction terms are presented in Additional file 1:
Tables S1 + S2, and predicted participation rates forschool breakfast and school lunch by CEP participation
status are shown in Fig. 1.
Based on comparison of predicted participation rates,
there was no association between the community foodenvironment and school breakfast participation rates
in schools participating in CEP (P-value for difference = 0.80), whereas such an association was detectedamong schools not participating in CEP. Specifically,
school breakfast participation rates were significantly
lower among non-CEP schools surrounded by a highdensity of unhealthy food outlets (predicted participation
rate = 53.3%) than among non-CEP schools surrounded
by a low-density of unhealthy food outlets (predicted

Page 5 of 13

participation rate = 56.9%, P-value for difference = 0.049).
Nonetheless, the interaction term testing whether CEP
modified the relationship between the density of the foodenvironment and breakfast participation was not statistically significant (B = 0.07, P = 0.62).
The association between school lunch participation
rates and density of the community food environment
did not differ by school CEP participation (B = 0.09,
P = 0.18). There were no significant relationships between
the community food environment and school lunch participation rates for CEP schools nor for non-CEP schools.
Models including 3-way interaction terms between thecommunity food environment around schools, school
CEP participation, and school level assessed whether
relationships displayed in Fig. 1 differed by school level.
Results from regression models with 3-way interaction
terms are presented in Additional file 1: Tables S3 + S4,
and predicted participation rates for school breakfast by
CEP participation status and school level are shown in
Fig. 2. Estimates from the model with school breakfast
participation rate as the outcome showed that differences in school breakfast participation by the healthfulness of the community food environment that wereobserved among non-CEP schools were mostly attributable to middle/high schools. Among non-CEP middle/
high schools, school breakfast participation rates weresignificantly lower for schools surrounded by a highdensity of unhealthy food outlets (predicted participation
rate = 30.9%) than for schools surrounded by a lowdensity of unhealthy food outlets (predicted participation rate = 40.5%, P-value for difference < 0.001), whereas
school breakfast participation rates among elementary
schools surrounded by a high-density and a low-density
food environment were not significantly different fromone another (P-value for difference = 0.60). Associations
between school lunch participation rates and density of
unhealthy food outlets did not differ by school CEP status among elementary or middle/high schools; predicted
participation rates for school lunch by CEP participation
status and school level are shown in Additional file 2: Fig.
S1.
All estimates from sensitivity analyses including thosetreating time as a series of dummy variables and thoseusing 3-level mixed-effects linear regression models produced similar patterns of results (data not shown). Using
the 3-level mixed-effects model, school breakfast participation rates were similarly significantly lower among
schools not participating in CEP and surrounded by ahigh-density food environment than among non-CEP
schools surrounded by a low-density food environment(predicted difference in participation rate = 3.9%, P-value
for difference = 0.01). As in main presented results, nosignificant difference in participation in school breakfast

75.7

School lunch
participation
rate, %

69.5

58.5

79.9

65.7

9.8

High-den‑sity environ‑
ment

22.8

77.2

5.3

Majority
2.4
Non-Hispanic
White/nomajority race/
ethnicity

19.3

39.0

570 (342)

0.6 (0.3)

0.7 (0.1)

Middleor highschoolStudent
enrollment

Competi‑tive food—healthy scale

NSLP—
healthy scale

0.6 (0.1)

0.6 (0.3)525 (346)

27.3

72.7

0.0

63.6

36.4

6.1

93.9

0.7 (0.1)

0.6 (0.2)565 (291)

32.2

67.7

4.6

42.5

53.9

18.5

81.5

68.9

54.2

0.8 (0.1)

0.6 (0.2)

571 (356)

27.6

72.4

6.9

65.5

27.6

10.3

89.7

78.8

60.1

0.7 (0.1)

0.7 (0.2)586 (322)

31.4

68.6

2.9

45.7

51.4

17.1

82.9

67.8

54.0

CEP (n = 29) Non-CEP(n = 70)

SY 2016–2017
Mean (SD) or %0.8 (0.1)

0.6 (0.2)568 (358)

27.6

72.4

0.0

69.0

31.0

10.3

89.7

77.3

57.0

0.7 (0.1)

0.7 (0.3)

578 (318)

30.6

69.4

4.2

47.2

48.6

13.9

86.1

68.2

56.3

CEP (n = 29) Non-CEP(n = 72)

SY 2017–2018
Mean (SD) or %0.8 (0.1)

0.6 (0.2)569 (318)

30.8

69.2

0.0

69.2

30.8

7.7

92.3

69.8

54.4

0.8 (0.1)

0.7 (0.2)

634 (294)

26.7

73.3

8.9

37.8

53.3

17.8

82.2

78.0

60.8

CEP (n = 39) Non-CEP(n = 45)

SY 2018–2019
Mean (SD) or %0.7 (0.1)

0.6 (0.2)562 (379)

34.1

65.9

2.3

72.7

25.0

13.6

86.4

77.6

55.7

0.8 (0.1)

0.7 (0.2)625 (315)

27.7

72.3

8.5

38.3

53.2

17.0

83.0

71.7

61.0

CEP (n = 44) Non-CEP(n = 47)

SY 2019–2020
Mean (SD) or %(2024) 22:287

0.7 (0.1)

0.6 (0.2)545 (257)

80.7

Elementary 61.0
schoolSchool level, %

47.4

56.1

Majority
Hispanic

47.4

41.5

MajorityBlack

School majority race/ethnicity, %90.2

Low-den‑sity environ‑
ment

Community food environment around school, %61.5

CEP (n = 33) Non-CEP(n = 65)

CEP (n = 41) Non-CEP(n = 57)

Schoolbreakfast
participation
rate, %

SY 2015–2016
Mean (SD) or %SY 2014–2015
Mean (SD) or %Sample characteristics

Table 2 Descriptive characteristics of schools by participation in the Community Eligibility Provision (CEP) for each of the six school years (SYs) of collected d­ ataaMelnick et al. BMC Medicine
Page 6 of 13

73.2

Breakfast
servedin classroom,
% yes

a

79.0

85.7

78.8

58.2

75.4

83.3

75.9

61.2

81.4

82.4

CEP (n = 29) Non-CEP(n = 70)

SY 2016–2017
Mean (SD) or %Schools were observed up to 6 times spanning between SY 2014–2015 and SY 2019–2020

FRPM free or reduced-price meal

85.8

CEP (n = 33) Non-CEP(n = 65)

CEP (n = 41) Non-CEP(n = 57)

Students
eligiblefor FRPM, %SY 2015–2016
Mean (SD) or %SY 2014–2015
Mean (SD) or %Sample characteristics

Table 2 (continued)

79.3

59.3

81.9

84.1

CEP (n = 29) Non-CEP(n = 72)

SY 2017–2018
Mean (SD) or %

84.6

63.1

71.1

74.6

CEP (n = 39) Non-CEP(n = 45)

SY 2018–2019
Mean (SD) or %

81.8

70.1

70.2

83.6

CEP (n = 44) Non-CEP(n = 47)

SY 2019–2020
Mean (SD) or %Melnick et al. BMC Medicine(2024) 22:287
Page 7 of 13

Melnick et al. BMC Medicine(2024) 22:287

Page 8 of 13

Table 3 Results from regression ­modelsa assessing predictors of average daily participation rates in school breakfast and school lunch
among 126 schools observed over a 6-year p­ eriodb
Predictor variable

Coef

95% CI

P-value(0.02, 0.22).02

School breakfast
Participation in CEP (ref: No)   Yes0.12

Community food environment around school (ref: low-density)  High-density− 0.08(− 0.18, 0.02).11

School year

0.00(− 0.02, 0.02).88− 0.67(− 0.82, − 0.51)< .01

School level (ref: elementary school)   Middle or high school
School majority race/ethnicity (ref: majority Black)   Majority Hispanic

0.08(− 0.04, 0.19).19   Majority non-Hispanic white/Mixed

0.14(0.00, 0.28).06

Enrollment (in hundredths)

0.00(− 0.03, 0.02).75

Competitive food healthfulness scale

0.06(− 0.10, 0.25).50

NSLP healthfulness scale− 0.07(− 0.39, 0.25).67

Proportion of students eligible for ­FRPMa

0.30(0.09, 0.51)

< .01

0.21

(0.13, 0.30)< .01(0.7, 0.19)< .01

Breakfast served in classroom (ref: No)   YesSchool lunch
Participation in CEP (ref: No)   Yes0.13

Community food environment around school (ref: low-density)  High-density− 0.01(− 0.08, 0.07).88

School year

0.02

(0.01, 0.03)< .01− 0.45(− 0.60, − 0.29)

< .01
.02

School level (ref: elementary school)   Middle or high school
School majority race/ethnicity (ref: majority Black)   Majority Hispanic

0.10(0.02, 0.18)   Majority non-Hispanic white/Mixed

0.14(0.05, 0.24)< .01− 0.04(− 0.07, − 0.02)< .01

Enrollment (in hundredths)
Competitive food healthfulness scale score− 0.02(− 0.01, 0.07).65

NSLP healthfulness scale score0.03(− 0.14, 0.20).71

Proportion of students eligible for FRPM

0.21

(0.07, 0.34)

< .01

0.04

(− 0.02, 0.10).15

Breakfast served in classroom (ref: No)   YesFRPM free or reduced-price meala

Generalized linear regression models for panel data with a binomial distribution, probit link function, and robust standard errorsbSchools could be observed between 1 and up to 6 school years if they were open during all 6 observed school years. Total school-year observations = 571

among non-CEP schools was detected (P-value fordifference = 0.85).

Discussion
In this study, we examined whether associations between
the density of unhealthy food outlets around schools
and participation rates in school breakfast and in school
lunch differ by school CEP participation, using longitudinal data collected over a 6-year period following
the implementation of the policy authorization of CEP.
While there was no observed moderation effect of CEP

participation on the association between the community
food environment around schools and participation inschool meals, findings from models including interaction
terms between school CEP participation and the community food environment around schools showed thatamong schools not participating in CEP, predicted school
breakfast participation rates were significantly lower
when there was a high density of unhealthy food outlets
surrounding schools. In contrast, there were no differences based on the community food environment among
schools participating in CEP. Models including 3-way

Melnick et al. BMC Medicine(2024) 22:287

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Fig. 1 School participation in the Community Eligibility Provision (CEP) as a moderator of the relationship between the community foodenvironment around schools and participation rates in school breakfast and in school lunch. Error bars represent 95% confidence i­ntervals1

interaction terms indicated that the observed differencesin school breakfast participation rates by the community food environment around schools among non-CEP
schools were mostly attributable to middle/high schools.
Study findings with respect to school breakfast participation build on previous studies showing that increased
density of food outlets nearby schools is associated withdecreased participation in school meals [14, 15] and that
CEP participation is associated with higher school meal
participation rates [3, 20]. Results from the current study
did not find an overall association between the density offood outlets surrounding schools and school meal participation, as was observed in previous literature (Mirtcheva

and Powell) [14]. It is worth noting that the Mirtcheva
and Powell study relied upon data collected prior to theauthorization of CEP and that in our data we observed an
association between the food environment and meal participation in non-CEP schools. Our results, which showthat school participation in CEP reduces the influenceof unhealthy food outlets surrounding schools on school
breakfast participation rates, suggest that CEP participation may have benefits for student health by reducing thepurchasing of foods from nearby outlets. Importantly,
the food outlets used to characterize the community
food environment around schools in this study (limited-service (i.e., fast-food) restaurants and convenienceMelnick et al. BMC Medicine(2024) 22:287

Page 10 of 13

Fig. 2 Results from a three-way interaction between school participation in the Community Eligibility Provision (CEP), the community foodenvironment around schools, and school level for participation rates in school breakfast. Error bars represent 95% confidence i­ntervals1

stores) largely sell unhealthy food items [39, 40]. Previous
research has also shown that a higher density of food outlets surrounding schools is associated with less healthy
food intake [41, 42] and higher BMI among students [43].
In regard to health equity, students from disadvantaged
households are more likely to attend schools with higher
densities of surrounding unhealthy food outlets [11,44–46]. For instance, D’Angelo and colleagues showedthat low-income and Hispanic students are more likely toattend schools surrounded by fast-food outlets [45] andZenk and Powell found that schools within lower income
neighborhoods were surrounded by comparatively more
fast-food restaurants and convenience stores [46].

Regression models did not detect similar differencesin associations between the community food environment and participation rates in school lunch according
to school CEP participation. This may occur because allschools within the analytical sample have closed campus policies during lunch, and students are not allowed
to leave the school during lunch periods. Because of
these policies, participation in school lunch may be lessinfluenced by the external community food environment
around schools, whereas breakfast participation may
face more competition from surrounding food outlets.
Future studies capturing schools’ open campus lunchpolicies would provide additional insights into potential

Melnick et al. BMC Medicine(2024) 22:287

influences of surrounding community food environments
on school lunch participation.
A primary strength of our study is that all schools
within the sample were predominantly low-income,with high rates of free and reduced-price meal eligibility.
As such, we are able to assess potential benefits of CEP
participation by comparing participating schools with
non-participating but likely eligible schools. Evidence ofthe benefits associated with CEP participation are consequential because large numbers of CEP-eligible schools
do not participate in CEP; for example, 48% of eligible
schools in New Jersey did not participate in CEP in SY
2020–2021 [31]. Policies that encourage CEP participation among eligible schools, such as providing higher
meal reimbursement rates to schools and simplifying
qualification processes, may increase the reach of the
program and its associated benefits for students [47–49].
Findings also suggested that the presence of food outlets near schools more strongly influence school breakfast participation among older children and adolescentscompared to younger children. We speculate that thismay occur because older children and adolescents havecomparatively higher levels of autonomy and access tofunds to purchase food from nearby outlets than younger
children [13]. Consideration of policy actions to expand
the reach of CEP may be therefore particularly warranted
for middle and high schools.
Limitations

There are some study limitations that should be noted.
First, analyses were based on four school districts, andbecause CEP participation decisions are often made at the
district level, there may be limited variability in CEP participation across schools within a district. To address this,we conducted sensitivity analyses that adjusted for nestingof schools within school districts and these produced similar findings. Second, middle and high schools were combined into a single category because of the limited sample
size. Third, because data were collected at the school level,
we cannot assess the contribution of food outlets surrounding children’s homes, which may differ in healthfulness from those in close proximity to their schools.
However, a previous study conducted within the sameschool districts indicated that most students live very close
to the schools they attend, with 28% of students living
within 0.25 miles of the school they attended, and another
29% of students living between 0.25 and 0.5 miles of theschool they attended [35]. Fourth, while the schools in our
sample were low-income, with high rates of eligibility forfree and reduced-price meals, we were not able to obtain
the identified student percentages for non-CEP participating schools for earlier years in the sample. However, based

Page 11 of 13

on the 2019–2020 data, all schools in the sample in thatyear had identified student percentages that would makethem eligible for CEP. Finally, the majority of schools
within the sample served a large proportion of racial/ethnic minority students and were located within urban settings. Consequently, findings may not be generalizable toall students attending schools within the US or abroad.

Conclusions
This study provides compelling evidence that schools’
CEP participation may lessen the detrimental influenceof unhealthy food environments on school breakfast
participation rates—particularly among middle/highschools, which enroll older children and adolescents.
Findings provide evidence for the benefits of the CEP
program and provision of free school meals to children
and suggest that policy actions to increase the reach of
the program may benefit students. The benefits of policy
actions to increase the reach of CEP may be heightenedamong schools serving older children and adolescents.
Abbreviations
CEP	Community Eligibility Provision
FRPM	Free or reduced-price meals
SY	School year

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03498-6.
Additional file 1: Tables S1-S4. Table S1 – [School breakfast participa‑tion by community food environment and CEP participation], Table S2– [School lunch participation by community food environment and CEP
participation], Table S3 – [School breakfast participation by community
food environment, CEP participation, and school level], Table S4 – [School
lunch participation by community food environment, CEP participation,and school level].
Additional file 2: Fig. S1. Predicted school lunch participation rates bycommunity food environment, CEP participation, and school level.
Acknowledgements
Not applicable.
Authors’ contributions
EMM ran the analyses and drafted the manuscript. FA provided analyticalsupport. PO-V and MY conceptualized the study and supervised the acquisi‑tion of the data. All authors made substantial contributions to conception
and design, interpretation of results, and critically revising the manuscript. All
authors read and approved the final manuscript.
Funding
This work was supported by the Robert Wood Johnson Foundation, theEunice Kennedy Shriver National Institute of Child Health and Human Devel‑
opment, National Institutes of Health (1R01HD071583-01A1), and the National
Heart, Lung, and Blood Institute, National Institutes of Health (1R01HL13781401). The funders had no role in the design, analysis or writing of this article.
Availability of data and materials
The datasets generated and/or analyzed during the current study are notpublicly available because the data are currently being utilized for additional

Melnick et al. BMC Medicine(2024) 22:287

Page 12 of 13

analyses and ongoing research, but are available from the corresponding
author on reasonable request.
14.

Declarations
Ethics approval and consent to participate
The Arizona State University and Rutgers University Institutional Review
Boards deemed this study (STUDY00015798) exempt and approved all study
protocols.
Consent for publication
Not applicable.

15.
16.

17.

Competing interests
The authors declare that they have no competing interest.
18.
Received: 4 January 2024 Accepted: 24 June 2024
19.

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